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Christian Hotz-Behofsits

Publications and source records attributed to Christian Hotz-Behofsits.

2 recordsLinked to original sources

Does TikTok Promote or Cannibalize Music Streaming? Estimands and Identification with Heavy-Tailed Outcomes

We study how TikTok affects demand for music on paid streaming platforms. We use Universal Music Group's (UMG) global withdrawal of its catalog from TikTok as a quasi-natural experiment. Recent work using this setting reaches mixed conclusions about whether TikTok promotes or cannibalizes streaming demand. We show that these findings can be reconciled by making the estimand explicit: with heavy-tailed exposure and outcomes, common difference-in-differences (DiD) implementations in levels, logs, and Poisson answer different economic questions. In our data, the top 10% of songs account for 96% of TikTok creations and 76% of Spotify streams, which makes the distinction between the typical song and the economically consequential song central. We find that removing TikTok access lowers Spotify demand for UMG titles, with losses concentrated among viral songs and little economically meaningful change for the long tail. Because the viral head accounts for a disproportionate share of listening and revenue, these losses drive aggregate implications. A TikTok creator-side analysis shows that some activity reallocates toward non-UMG audio when UMG content is unavailable. This substitution is limited in magnitude but economically relevant for interpreting the treatment effect because streaming compensation depends on relative stream shares. Finally, using the 2025 U.S. TikTok outage, which affected all labels symmetrically and is not subject to the label-specific spillover concern as the UMG withdrawal, we find corroborating evidence that disruptions to TikTok access reduce monetized streaming. We also provide a practitioner companion that guides the choice of DiD estimands, estimators, and diagnostics in heavy-tailed outcome settings.

econ.GN

Predicting crypto-currencies using sparse non-Gaussian state space models

In this paper we forecast daily returns of crypto-currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non-normality of the measurement errors and sharply increasing trends, we develop a time-varying parameter VAR with t-distributed measurement errors and stochastic volatility. To control for overparameterization, we rely on the Bayesian literature on shrinkage priors that enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner. Using around one year of daily data we perform a real-time forecasting exercise and investigate whether any of the proposed models is able to outperform the naive random walk benchmark. To assess the economic relevance of the forecasting gains produced by the proposed models we moreover run a simple trading exercise.

econ.EM